Multi-discriminator image inpainting algorithm based on hybrid dilated convolution network

In order to effectively solve the defects of large-area semantic information loss, the appearance of blurred edges and artifact or distortion when the lost area size is large and the shape is irregular under complex image background, a multiple discriminator based on hybrid dilated convolution network image inpainting algorithm was proposed. First, the image to be restored was input into a fuzzy convolution network based on hybrid dilated convolution layer, which the rough restoration was carried out on account of reconstruction loss. Subsequently, the rough restoration was fed into the bi-parallel convolution network, which contained the convolution path of HDC layer and a convolution path of the parallel contextual attention layer. After decoding and de-convolution, the outputs of the two parallel paths were sent to the discriminator for discrimination and optimization. Finally, in the process of network optimization, the global discriminator, local discriminator and central discriminator were utilized to enhance the overall and local semantic consistency and the detail features of the inpainted image. The proposed algorithm was trained and tested on the open face dataset CelebA and landscape dataset Places2. The experimental results show that the proposed method can enhance the accuracy of image detail and effectively avoid the restoration distortion. The proposed method is superior to the four classical algorithms used for comparison in visual effect, peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and average error of repair. © 2021, Editorial Board of Journal of Huazhong University of Science and Technology. All right reserved.